diff options
| -rw-r--r-- | tensorflow/python/keras/_impl/keras/estimator.py | 22 | ||||
| -rw-r--r-- | tensorflow/python/keras/_impl/keras/estimator_test.py | 24 |
2 files changed, 36 insertions, 10 deletions
diff --git a/tensorflow/python/keras/_impl/keras/estimator.py b/tensorflow/python/keras/_impl/keras/estimator.py index b922a6c683..c3c3fceb45 100644 --- a/tensorflow/python/keras/_impl/keras/estimator.py +++ b/tensorflow/python/keras/_impl/keras/estimator.py @@ -29,12 +29,14 @@ from tensorflow.python.estimator import run_config as run_config_lib from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib +from tensorflow.python.framework import tensor_util from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import models from tensorflow.python.keras._impl.keras import optimizers from tensorflow.python.keras._impl.keras.engine.base_layer import Layer from tensorflow.python.keras._impl.keras.engine.network import Network from tensorflow.python.keras._impl.keras.utils.generic_utils import CustomObjectScope +from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_module from tensorflow.python.ops import variables as variables_module @@ -55,6 +57,17 @@ def _cast_tensor_to_floatx(x): return math_ops.cast(x, K.floatx()) +def _convert_tensor(x): + """Create or cast tensor if needed.""" + if not tensor_util.is_tensor(x): + # x is a numpy array + x = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(x) + if check_ops.is_numeric_tensor(x): + # is_numeric_tensor returns False if provided with a numpy array + x = _cast_tensor_to_floatx(x) + return x + + def _any_variable_initalized(): """Check if any variable has been initialized in the Keras model. @@ -86,7 +99,7 @@ def _create_ordered_io(keras_model, estimator_io, is_input=True): if isinstance(estimator_io, (list, tuple)): # Case currently not supported by most built-in input_fn, # but it's good to have for sanity - return [_cast_tensor_to_floatx(x) for x in estimator_io] + return [_convert_tensor(x) for x in estimator_io] elif isinstance(estimator_io, dict): if is_input: if keras_model._is_graph_network: @@ -108,12 +121,12 @@ def _create_ordered_io(keras_model, estimator_io, is_input=True): 'It needs to match one ' 'of the following: %s' % ('input' if is_input else 'output', key, ', '.join(keras_io_names))) - tensors = [_cast_tensor_to_floatx(estimator_io[io_name]) + tensors = [_convert_tensor(estimator_io[io_name]) for io_name in keras_io_names] return tensors else: # Plain array. - return _cast_tensor_to_floatx(estimator_io) + return _convert_tensor(estimator_io) def _in_place_subclassed_model_reset(model): @@ -274,8 +287,7 @@ def _clone_and_build_model(mode, is_input=False) else: target_tensors = [ - _cast_tensor_to_floatx( - sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(labels)) + _convert_tensor(labels) ] if keras_model._is_graph_network: diff --git a/tensorflow/python/keras/_impl/keras/estimator_test.py b/tensorflow/python/keras/_impl/keras/estimator_test.py index 653cdc01e2..80fa87d041 100644 --- a/tensorflow/python/keras/_impl/keras/estimator_test.py +++ b/tensorflow/python/keras/_impl/keras/estimator_test.py @@ -30,6 +30,7 @@ from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.keras._impl import keras +from tensorflow.python.keras._impl.keras import backend as K from tensorflow.python.keras._impl.keras import testing_utils from tensorflow.python.keras._impl.keras.applications import mobilenet from tensorflow.python.keras._impl.keras.optimizers import SGD @@ -142,16 +143,20 @@ def randomize_io_type(array, name): def multi_inputs_multi_outputs_model(): - # test multi-input layer a = keras.layers.Input(shape=(16,), name='input_a') b = keras.layers.Input(shape=(16,), name='input_b') + m = keras.layers.Input(shape=(8,), dtype='bool', name='input_m') dense = keras.layers.Dense(8, name='dense_1') + a_2 = dense(a) + # Apply a mask + s_2 = keras.layers.Lambda(lambda k: + K.switch(k[0], k[1], K.zeros_like(k[1])))([m, a_2]) b_2 = dense(b) - merged = keras.layers.concatenate([a_2, b_2], name='merge') + merged = keras.layers.concatenate([s_2, b_2], name='merge') c = keras.layers.Dense(3, activation='softmax', name='dense_2')(merged) d = keras.layers.Dense(2, activation='softmax', name='dense_3')(merged) - model = keras.models.Model(inputs=[a, b], outputs=[c, d]) + model = keras.models.Model(inputs=[a, b, m], outputs=[c, d]) model.compile( loss='categorical_crossentropy', optimizer='rmsprop', @@ -352,18 +357,27 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): test_samples=50, input_shape=(16,), num_classes=2) + np.random.seed(_RANDOM_SEED) + (input_m_train, _), (input_m_test, _) = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(8,), + num_classes=2) + c_train = keras.utils.to_categorical(c_train) c_test = keras.utils.to_categorical(c_test) d_train = keras.utils.to_categorical(d_train) d_test = keras.utils.to_categorical(d_test) def train_input_fn(): - input_dict = {'input_a': a_train, 'input_b': b_train} + input_dict = {'input_a': a_train, 'input_b': b_train, + 'input_m': input_m_train > 0} output_dict = {'dense_2': c_train, 'dense_3': d_train} return input_dict, output_dict def eval_input_fn(): - input_dict = {'input_a': a_test, 'input_b': b_test} + input_dict = {'input_a': a_test, 'input_b': b_test, + 'input_m': input_m_test > 0} output_dict = {'dense_2': c_test, 'dense_3': d_test} return input_dict, output_dict |